RNA-SEQUENCING ANALYSIS IN B-CELL ACUTE …

RNA-SEQUENCING ANALYSIS IN B-CELL ACUTE LYMPHOBLASTIC

LEUKEMIA REVEALS ABERRANT GENE EXPRESSION AND SPLICING

ALTERATIONS

_______________________________________

A Thesis

presented to

the Faculty of the Graduate School

at the University of Missouri-Columbia

_______________________________________________________

In Partial Fulfillment

of the Requirements for the Degree

Master of Science

_____________________________________________________

by

OLHA KHOLOD

Dr. Kristen Taylor, Thesis Supervisor

MAY 2017

The undersigned, appointed by the Dean of the Graduate School, have examined the

thesis entitled

RNA-SEQUENCING ANALYSIS IN B-CELL ACUTE LYMPHOBLASTIC

LEUKEMIA REVEALS ABERRANT GENE EXPRESSION AND SPLICING

ALTERATIONS

Presented by OLHA KHOLOD

A candidate for the degree of Master of Science

And hereby certify that, in their opinion, it is worthy of acceptance.

____________________________________________

Kristen Taylor, Ph.D.

____________________________________________

Christine Elsik, Ph.D.

____________________________________________

Dmitriy Shin, Ph.D.

ii

ACKNOWLEDGEMENTS

First and foremost I would like to acknowledge my academic advisor Dr. Kristen

Taylor who gave me the opportunity to be trained in her laboratory. Throughout my

study, she contributed to a rewarding graduate school experience by giving me

intellectual freedom in research and inspiring me to pursue a career in science.

Additionally, I would like to thank my committee members Dr. Christine Elsik and Dr.

Dmitriy Shin for their guidance and encouragement. Especially, Dr. Elsik who trained me

to perform transcriptome data analysis and to program in Perl.

I also would like to acknowledge the many people I have worked with during the

past two years. I want to thank Marianne Emery for assisting me with edgeR analysis and

for her valuable advice regarding the processing of RNA-seq data. In addition, I would

like to thank Dr. Senthil Kumar for fruitful discussions about cancer epigenetics and

guidance in performing cell line treatment experiments. I also would like to acknowledge

my laboratory mates Alex Stuckel and Clayton Del Pico for their friendship and support.

I would like to thank the Fulbright Foreign Student Program for providing an

opportunity to obtain firsthand research experience in the United States and to meet with

amazing people from all over the world. I also want to thank my best friends Sopheak

and Xianglei for making me feel like home and for making me a better person. Finally, I

would like to express my very profound gratitude to my parents and to my elder sister for

providing me with unfailing support and continuous encouragement throughout my life

and career.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ................................................................................................ ii

LIST OF FIGURES ............................................................................................................ v

LIST OF TABLES ............................................................................................................. vi

PREFACE .......................................................................................................................... ix

Chapter 1 Literature Review ............................................................................................... 1

1.1 B-Cell Acute Lymphoblastic Leukemia ....................................................................1

1.1.1 Characteristics of B-ALL ................................................................................... 1

1.1.2 Abnormal B-Cell Development in Leukemogenesis .......................................... 2

1.1.3 Genetic Alterations in B-ALL ............................................................................ 4

1.1.4 Epigenetic alterations in B-ALL ....................................................................... 12

1.2 Alternative Splicing in B-ALL ................................................................................17

1.2.1 Characteristics of Alternative Splicing Events in Cancer ................................ 17

1.2.2 Alternative splicing isoforms in B-ALL ........................................................... 19

1.3 Rationale for Thesis .................................................................................................20

1.4 Experimental Aims and Hypothesis .........................................................................23

Chapter 2 RNA-Sequencing Analysis in B-cell Acute Lymphoblastic Leukemia Reveals

Aberrant Gene Expression and Splicing Alterations ........................................................ 25

iv

Abstract ..........................................................................................................................25

Introduction ....................................................................................................................26

Materials and Methods ...................................................................................................28

Results ............................................................................................................................35

Discussion ......................................................................................................................40

Conclusions ....................................................................................................................45

GENERAL DISCUSSION ............................................................................................... 63

BIBLIOGRAPHY ............................................................................................................. 65

VITA ................................................................................................................................. 93

v

LIST OF FIGURES

Figure 1. Schematic diagram of B-cell development stages, immunophenotype and major

transcription factors. ......................................................................................................... 46

Figure 2. Bar diagram represents distribution of uniquely mapped reads to human

genome UCSC hg19 (GRCh37)........................................................................................ 47

Figure 3. Average percentage of sequencing reads from 8 B-ALL and 8 healthy donors

that map to coding sequence exon (CDS), 5’ and 3’ untranslated regions (5’ and 3’UTR),

introns and intergenic regions. .......................................................................................... 48

Figure 4. The heatmap representing common gene isoforms for B-ALL patients

identified by custom Perl script. ....................................................................................... 49

Figure 5. The mechanistic network of the inferred upstream regulator TGFB1. Genes

presented in red are related to genes that up-regulated in B-ALL dataset. ....................... 50

Figure 6. The differentially expressed gene network with function in cell transformation.

Genes represented in red are upregulated in B-ALL group. ............................................. 51

Figure 7. The differentially expressed gene network with function in proliferation of

cancer cells. ....................................................................................................................... 52

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LIST OF TABLES

Table 1 Alternative splicing events in cancer. .................................................................. 53

Table 2 Patient characteristics. ......................................................................................... 55

Table 3 Top twenty upregulated and down-regulated genes in B-ALL patients versus

healthy donors. .................................................................................................................. 56

Table 4 Common transcripts that affected by DNA methylation ..................................... 58

Table 5 Gene ontology terms for common transcripts that affected by DNA methylation

........................................................................................................................................... 60

Table 6 Top canonical pathways identified by IPA .......................................................... 62

Supplementary Table 1 ..................................................................................................... 87






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NOMENCLATURE

5-Aza 5-aza-2-deoxycytidine

AS Alternative splicing

B-ALL B-cell acute lymphoblastic leukemia

CGI CpG island

CLP Common lymphoid progenitor

DE Differentially expressed genes

DMR Differentially methylated region

DNA Deoxyribonucleic acid

eRNA Enhancer RNA

FISH Fluorescence in situ hybridization

FPKM Fragments per kilobase of transcript per million mapped reads

GLM General linear model

HSC Hematopoietic stem cell

IPA Ingenuity pathway analysis

KB Knowledge Base

LMPP Lymphoid multipotent progenitor

MDS Multidimensional scaling

miRNA MicroRNA

NGS Next generation sequencing

PCR Polymerase chain reaction

viii

Pre-BCR Pre-B cell receptor

RNA-seq RNA-sequencing

RT-PCR Reverse transcription polymerase chain reaction

TF Transcriptional factor

TMM Trimmed mean of M-values

TR Transcriptional regulator

TSA Trichostatin A

UTR Untranslated region

WBC While blood cell

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PREFACE

B-cell acute lymphoblastic leukemia (B-ALL) is a neoplasm of immature

lymphoid progenitors and is the leading cause of cancer-related death in children. The

majority of B-ALL cases are characterized by recurring structural chromosomal

rearrangements that are crucial for triggering leukemogenesis, but do not explain all

incidences of disease. Therefore, other molecular mechanisms, such as alternative

splicing and epigenetic regulation may alter expression of transcripts that are associated

with the development of B-ALL. It is important to investigate alternatively spliced RNA

transcripts that may be affected by aberrant DNA methylation in B-ALL to gain a better

understanding of the pathogenesis of this disease.

The goal of this research proposal is to characterize the transcriptome landscape

of patients with B-ALL using high throughput RNA-sequencing (RNA-seq) analysis.

Specifically, the study aims to identify particular genes and their isoforms that might be

controlled by aberrant DNA methylation in B-ALL and contribute to the development of

this disease. By analyzing transcriptional patterns between B-ALL patients and healthy

cord blood donors differentially expressed and alternatively spliced RNA transcripts have

been identified. By examining differentially expressed genes with Ingenuity pathway

analysis, the most significant signaling pathways and gene functions have been

annotated. By analyzing causative gene networks, novel upstream regulators have been

determined for B-ALL patients. Finally, a mechanistic study has been conducted using an

x

in vitro B-ALL model to investigate if aberrant DNA methylation affects alternatively

spliced genes associated with this disease.

In this thesis, chapter 1 will introduce abnormal B-cell development in

leukemogenesis and discuss in detail the genetic abnormalities that are hallmarks of B-

ALL. Chapter 1 will also introduce aberrant epigenetic modifications including DNA

methylation, histone modifications, and non-coding RNAs that have been identified in B-

ALL patients to date. Alternative splicing alterations associated with B-ALL will be also

described in chapter 1. Chapter 2, the research chapter, investigates the transcriptional

regulators and signaling pathways that likely orchestrate the regulation of differentially

expressed genes identified in the study. Finally, chapter 2 includes a mechanistic study

utilizing the Nalm 6 cell line to determine the role of DNA methylation on the expression

of alternatively spliced transcripts.

Our pathway-centric approach may help to explore and characterize novel

aberrant gene expression patterns for B-ALL patients, thereby complementing previous

research findings aimed at deciphering the pathogenesis of B-ALL. Moreover, identified

alternatively spliced transcripts may help better understand the molecular basis of post-

transcriptional gene regulation in the context of B-ALL. By inferring a role for DNA

methylation in the expression of alternatively spliced isoforms, new avenues might be

explored for improved diagnosis, management and treatment of B-ALL patients in the

future.

1

Chapter 1 Literature Review

1.1 B-Cell Acute Lymphoblastic Leukemia

1.1.1 Characteristics of B-ALL

B-cell lymphoblastic leukemia (B-ALL) is a malignant neoplasm derived from B-

cell progenitors. B-ALL is common among children, with peak prevalence between the

age of 2 and 5 (Pui, Robison, & Look, 2008). The symptoms of B-ALL include fatigue

and paleness from anemia, bruising due to thrombocytopenia, and frequent infection

caused by neutropenia (Hunger & Mullighan, 2015). Outcome for pediatric cases with B-

ALL has significantly improved over the last 2 decades; the 5-year survival rate is greater

than 80%. In adults with B-ALL, existing treatments have been less effective, with a

disease related mortality of approximately 60% (Redaelli, Laskin, Stephens, Botteman, &

Pashos, 2005).

The precise pathogenic events leading to the development of B-ALL are still

undetermined. Less than 5% of the cases are associated with inherited, predisposing

genetic syndromes, such as Down’s syndrome, Bloom’s syndrome, ataxia-telangiectasia,

and Nijmegen breakage syndrome (Pui et al., 2008). Common genetic events leading to

the development of B-ALL include chromosomal translocation, hyperdiploidy and

deregulation of proto-oncogenes (Mullighan, 2012). Due to recently developed next-

generation sequencing (NGS) technologies, such as transcriptome sequencing, and

whole-genome sequencing, the number of genetic alterations identified in B-ALL

patients has increased excessively (Roberts & Mullighan, 2015). However, in

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experimental models, commonly occurring genetic aberrations do not alone induce

leukemia, pointing that additional genetic or epigenetic changes are required.

Identification of these additional genetic and epigenetic alterations is crucial for better

understanding B-ALL pathogenesis and development.

1.1.2 Abnormal B-Cell Development in Leukemogenesis

B cells are derived from pluripotent hematopoietic stem cells (HSCs) in the bone

marrow through sequential stages of cell differentiation, including lymphoid multipotent

progenitors (LMPPs), common lymphoid progenitors (CLPs), early pro-B cells, pro-B

cells, pre-B cells, and mature B cells (Figure 1). Knowledge of the normal sequence of

antigen acquisition is crucial, because B-ALL arises from B-cell progenitors that reflect

arrested stages of B-cell maturation. CLPs are characterized by the presence of the cell

surface antigens CD34 and CD10. During the transition from CLP to early pro-B cells,

CD10 is lost and CD19 is gained; CD34, CD10 and CD19 are positive in pro-B cells and

pre-B cells express only CD10 and CD19. In the final transition to immature B-cells,

lymphoblasts begin to express CD20 and IgM in addition to CD10 and CD19 markers

(Zhou, You, Young, Lin, Lu, Medeiros, & Bueso-Ramos, 2012).

Transcriptional factor E2A triggers early B-lineage development through

regulating the downstream transcription factors EBF1 and PAX5. Both EBF1 and PAX5

are critical for maintaining B-lineage maturation, as abscission of PAX5 and reduced

EBF1 expression result in de-differentiation to immature progenitor cells (Pongubala et

al., 2008). Enforced expression of CEBPA, a transcription factor crucial for myeloid

3

development, in progenitor B cells, inhibits B-lineage-specific genes and conversion into

macrophages in vitro (Bussmann et al., 2009). Dysregulation of some of these

transcription factors (TFs) in B-ALL has been long known because their encoding genes

are involved in cytogenetic abnormalities, but the broad disruption of B-cell development

in more than 40% of B-ALL cases has only been recognized recently by genome-wide

genetic analysis (Mullighan et al., 2007).

Early B-lineage development also depends on signal transduction initiated by the

interleukin (IL)-7 receptor in pro-B cells and the pre-B-cell receptor (pre-BCR) in pre-B

cells. The IL-7 receptor consists of common γ-chain and an IL-7Rα subunit (encoded by

IL7R gene), while pre-BCR consists of 2 Igμ chains and 2 surrogate light chains. Effects

of IL-7R activation are mediated through the JAK-STAT5 pathway (Hennighausen &

Robinson, 2008) and in context of this signaling network transcriptional factor STAT5

upregulates EBF1 and PAX5 expression (Dias, Silva, Cumano, & Vieira, 2005;

Hirokawa, Sato, Kato, & Kudo, 2003) which results in maintaining of pro-B-cell state.

When the pro-B-cell stage has been established, B-cell progenitors undergo

rearrangement in heavy chain immunoglobulin IgH. After a successful IgH

rearrangement, IL-7R acts in combination with other factors, including pre-BCR, to

promote expansion of early pre-B cells through an ERK/MAPK-dependent pathway

(Fleming & Paige, 2001). Disruption in the pre-BCR component Igμ leads to a complete

B-cell developmental block at the pro-B-cell to pre-B-cell transition (Kitamura, Roes,

Kuhn, & Rajewsky, 1991). Pre-BCR signaling also activates a negative feedback loop

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through suppressing IL-7Rα expression and attenuating STAT5 activation (Marshall,

Fleming, Wu, & Paige, 1998). Dysregulation of the signal transduction cascade can be

directly oncogenic and likely contributes to poor clinical outcome.

1.1.3 Genetic Alterations in B-ALL

Multiple genetic alterations have been discovered in B-ALL patients and used for

risk classification and treatment assignment. Chromosome translocations, such as E2A-

PBX1, TEL-AML1 and BCR-ABL1 occur in approximately 80% of children and 60% to

70% of adults with B-ALL. These chromosomal abnormalities can be detected by routine

cytogenetic analysis and interphase fluorescence in situ hybridization (FISH). Smaller

genetic aberrations, such as IKZF1, PAX5 and CDKN2A/B deletions can be determined

by polymerase chain reaction (PCR). Combined with high-throughput DNA sequencing

and gene expression profiling, genome-wide studies of B-ALL have uncovered

remarkable associations between B-ALL and disruptions of B-cell development, loss of

tumor suppressor activity, and aberrant signal transduction (Zhang, Mullighan, Harvey,

Wu, Chen, Edmonson, & Hunger, 2011; Zhou et al., 2012).

E2A translocations

E2A is a basic helix-loop-helix transcription factor located on chromosome

19p13. E2A is necessary for initiation of B-cell development and is crucial for B-cell

differentiation (LeBrun, 2003). The most common translocation involving the E2A gene

is t(1;19)(q23;p13). This genetic abnormality appears in approximately 5% of B-ALL

cases and is more prevalent among children. The resulting fusion protein consists of

5

transactivation domains of E2A and the DNA-binding homeodomain of PBX1 (Hunger,

1996). The oncogenic effect of E2A-PBX1 chimeric protein is a result of the upregulation

of the BMI1 gene (Smith et al., 2003), a transcriptional repressor that participates in

hematopoietic stem-cell self-renewal (Park et al., 2003). A second E2A associated

translocation, t(17;19), occurs rarely among children. This variant consists of

transactivation domains of E2A and the leucine zipper dimerization domain of HLF. The

aberrant upregulation of LMO2 and BCL2 results from the activation of the E2A-HLF

fusion protein (De Boer et al., 2011; Hirose et al., 2010). With modern chemotherapy,

patients with B-ALL associated with the E2A-PBX1 translocation have a favorable

outcome, but B-ALL cases associated with t(17;19) have a poor prognosis (Hu et al.,

2016).

BCR-ABL1 (Philadelphia chromosome)

The tyrosine kinase BCR-ABL chimeric protein is the product of the Philadelphia

chromosome, which is formed due to the reciprocal translocation t(9;22)(q34;q11) that

opposes the ABL oncogene 1 on chromosome 9 with the BCR gene on chromosome 22

generating the BCR-ABL1 fusion gene (López-Andrade et al., 2015). This protein has

constitutive ABL1 kinase activity and localizes in the cell nucleus. It has been shown that

BCR-ABL1 alone is sufficient to induce cancerous transformation in pre-B cells in a

mouse model and that this process requires the activation of SRC kinase (Huettner,

Zhang, Van Etten, & Tenen, 2000). This translocation rarely occurs in children but it is

the most common (approximately 25%) cytogenetic abnormality in adults (Moorman,

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2016). Depending on location of the breakpoint in the BCR gene, BCR-ABL fusion

proteins of different molecular weights can be formed. BCR-ABL p210 can be seen in

24% to 50% of adult Philadelphia positive (Ph+) B-ALL. A shorter form, p190,

predominates in pediatric Ph+ B-ALL and 50% to 76% of adult Ph+ B-ALL. BCR-ABL

p230 usually is not observed in B-ALL. Comparisons of adult Ph+ B-ALL patients with

p210 or p190 variants showed consistency in the presence of additional cytogenetic

abnormalities, white blood cell (WBC) count or outcome (Rieder, Banta, Köhrer,

McCaffery, & Emr, 1996). B-ALL associated with BCR-ABL1 shows a common

immunophenotype, that being CD34+, CD10+, and CD19+; myeloid markers are positive

in up to 71% of cases in adults. B-ALL associated with BCR-ABL1 has a very poor

outcome with a 5-year overall survival of less than 10% (Moorman et al., 2010).

Mixed lineage leukemia rearrangements (MLL)

The mixed lineage leukemia (MLL) gene is involved in a wide range of leukemia-

associated translocations (Meyer et al., 2009). The most common chromosomal

rearrangement involving MLL in B-ALL is t(4;11)(q21;q23), which results in an MLL-

AF4 fusion gene. This particular translocation is associated with very poor prognosis for

infants under 1 year, the vast majority of whom have a relapse and die of progressive

disease. However, for children 1-9 years old or those 10 years of age or older

t(4;11)(q21;q23) is correlated with more favorable prognosis (Pui et al., 2003). MLL gene

rearrangements have been diagnosed in approximately two thirds of infantile ALL cases,

and MLL-AF4 consists of more than 50% of the rearrangements (Pieters et al., 2007). In

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adults, MLL-AF4 occurs in 4% to 8% of ALL in general (Moorman et al., 2010; Wetzler

et al., 1999), but it is more frequent (24%) in patients who have received chemotherapy

for other malignancies (Tang, Neufeld, Rubin, & Müller, 2001). Pro-B ALL with

t(4;11)/MLL rearrangements is most often myeloid antigen-positive disease (including

expression of CD15) (Chiaretti, Zini, & Bassan, 2014). Patients with B-ALL associated

with MLL-AF4 have a high risk of relapse.

ETV6-RUNX1 (TEL-AML1)

ETV6, located on chromosome 12p13 previously known as TEL is an ETS family

transcriptional repressor and is frequently rearranged or fused with other genes in human

leukemias of myeloid or lymphoid origins (Zhang et al., 2015). RUNX1, located on

chromosome 21q22 and previously known as AML1, is a transcription factor that

participates in hematopoietic development at an early embryonic stage as well as B-cell

differentiation in adult hematopoiesis (Ichikawa et al., 2004) results in the ETV6-

RUNX1fusion protein, t(12;21)(p13;q22), consists of the N-terminal non-DNA-binding

region of ETV6 combined with RUNX1. Enforced expression of ETV6-RUNX1 in HSCs

results in expansion of multipotent progenitors and partial arrest of B-cell development at

the pro-B cell stage (Tsuzuki, Seto, Greaves, & Enver, 2004). ETV6-RUNX1 is the most

frequent alteration in pediatric B-ALL, present in approximately 30% of cases, but is rare

in adults (Raynaud et al., 1996). Secondary genetic abnormalities including loss of the

ETV6 allele and other genes in the B-cell development pathway are frequently identified

at the time of diagnosis of B-ALL (Hong et al., 2008; Mullighan et al., 2009). B-ALL

8

associated with t(12;21) is usually positive for CD10, CD19, CD34, and the myeloid

associated antigen CD13. Patients with B-ALL associated with ETV6-RUNX1 have a

highly positive outcome.

Immunoglobulin heavy-chain locus (IGH@)

Recurrent translocations of the IGH@ locus in B-ALL are relatively rare but have

been well documented (Dyer et al., 2010). Fusions of IGH@ with each of the 5 members

of the CEBP family have been reported in B-ALL in children and adults (Akasaka et al.,

2007). The fusion with CEBPD, as a result of t(8;14)(q11;q32), is the most common

(Lundin, Heldrup, Ahlgren, Olofsson, & Johansson, 2009). This translocation occurs

mostly in children, either as a sole acquired abnormality or in conjunction with t(9;22) or

Down syndrome. Partners of IGH@ translocation also include ID4 (Russell et al., 2008),

erythropoietin receptor (Russell et al., 2009), CRLF2, IL3 (Grimaldi & Meeker, 1989),

and miRNA-125-b-1 (Sonoki, Iwanaga, Mitsuya, & Asou, 2005). The IGH-IL3

translocation, t(5;14)(q31;q32), commonly results in eosinophilia. The IGH-MYC

rearrangement, t(8;14)(q24;q32), and IGH-BCL2 translocation, t(14;18)(q32;q21) were

identified in 7% and 4% of adult patients with B-ALL, respectively. Patients with B-ALL

associated with t(8;14)(q24;q32) or t(14;18)(q32;q21) have a very poor outcome

(Moorman et al., 2010).

Numerical chromosomal abnormalities

Several chromosome abnormalities have been identified in B-ALL, including

hyperdiploidy, hypodiploidy, near-haploidy and complex karyotypes. Hyperdiploidy

9

occurs predominantly in pediatric B-ALL, accounting for nearly 40% of cases, and is

associated with favorable prognosis. Hypodiploidy, near-haploidy, and complex

karyotypes are rare in childhood B-ALL, but their frequency increases with age.

Together, these abnormalities account for approximately 15% of B-ALL cases in patients

older than 60 years. Hypodiploidy, near-haploidy, and a complex karyotype are

associated with poor outcome, with less than 20% of patients surviving for 5 years

(Moorman et al., 2010).

Intrachromosomal amplification of chromosome 21

Intrachromosomal amplification of chromosome 21 (iAMP21) is defined as the

presence of 3 or more copies of the RUNX1 gene (Harrison, 2011). The 5.1-Mb common

region of amplification contains RUNX1, mIR-802, and genes in the Down syndrome

critical region. iAMP21 occurs in approximately 2% of childhood B-ALL, and these

malignancies have a common/pre-B immunophenotype (Harewood et al., 2003). B-ALL

with iAMP21 occurs with high frequency in B-ALL associated with Down syndrome.

Other genetic alterations associated with iAMP21 include deletion of RB1, CDKN2A,

IKZF1, and PAX5 (Rand et al., 2011). Patients with iAMP21 have relatively poor

prognosis if not treated with enhanced chemotherapy (Moorman et al., 2007).

IKZF1 deletion

IKZF1, located at 7p13-p11.1, encodes IKAROS, a zinc-finger containing DNA-

binding protein. IKAROS isoforms lacking N-terminal zinc-finger domains have

10

abnormal localization and function as a dominant negative of wild-type IKAROS.

Genome-wide single nucleotide polymorphism array analysis has shown that IKZF1

deletions are among the most common genetic lesions in high-risk B-ALL, present in

75% to 90% of BCR-ABL1+ B-ALL (Mullighan et al., 2008) and 29% of pediatric high-

risk BCR-ABL1 B-ALL (Mullighan et al., 2009). Deletions of IKZF1 are predominantly

monoallelic and are limited to the gene in approximately 40% cases (Mullighan et al.,

2008). Various patterns of deletions occur, but the most frequent deletions involve the N-

terminal zinc-finger domain of IKAROS and result in expression of dominant-negative

isoforms with cytoplasmic localization and oncogenic activity (Iacobucci et al., 2012).

IKZF1 deletion in B-ALL is associated with a high risk of relapse.

PAX5 deletion and translocation

PAX5 encodes a B-lineage specific transcription factor located at chromosome

9p13. PAX5 is among the most frequent targets of genetic alterations in B-ALL,

observed in approximately 30% of cases (Dang et al., 2015). There are several genetic

aberrations associated with PAX5 gene, including monoallelic deletions, translocations

and point mutations. Deletions are frequently associated with BCR-ABL1, E2A-PBX1,

and complex karyotype with secondary genetic changes (Coyaud et al., 2010). PAX5

rearrangements are relatively rare, occurring in 2.5% of B-ALL cases; at least 12

different fusion partners including TFs, structural proteins, and protein kinases have been

reported (Nebral et al., 2009). Deletion and mutation of other genes essential in B-cell

11

development, including EBF1, RAG1, RAG2, LEF1, and BLINK, are also frequently

detected in B-ALL (Mullighan et al., 2007).

CDKN2A/B deletion

CDKN2A and adjacent CDKN2B on chromosome 9p21 are tumor suppressor

genes that encode p16INK4a/p14ARF and p15INK4b, respectively. The proteins are

involved in controlling G1/S cell-cycle progression. In B-ALL, deletion of CDKN2A/B is

the most frequent genetic abnormality detected by genome-wide copy number alteration

and loss of heterozygosity analysis. These deletions are present in 21% to 36% pediatric

B-ALL (Mullighan et al., 2008; Kawamata et al., 2008), and nearly 50% of adult and

adolescent B-ALL (Paulsson et al., 2008). CDKN2A/B deletions are frequently associated

with BCR-ABL1 and E2A-PBX1 fusion, and are less frequently present in B-ALL

associated with ETV6-RUNX1, MLL translocation, or hyperdiploidy (Sulong et al.,

2009). CDKN2A/B deletion can be detected at initial diagnosis or acquired at relapse;

there is no difference in frequency between diagnosis and relapse, suggesting that

CDKN2A/B deletion is a secondary genetic event.

Janus kinase mutations

JAK is a protein tyrosine kinase and a key player in the JAK-STAT pathway.

Mutations in JAK1 and JAK2 were initially identified in B-ALL associated with Down

syndrome (Bercovich et al., 2008; Kearney et al., 2009). Heterozygous somatic mutations

of JAKs are seen in approximately 10% of non-Down syndrome B-ALL (Mullighan et

al., 2009). JAK mutations occur in highly conserved residues in the kinase and

12

pseudokinase domain and result in constitutive kinase activation. It appears that aberrant

kinase signaling requires interaction with a cytokine receptor, because ectopic expression

of ALL-associated JAK1 mutant alone fails to trigger STAT activation in the absence of

a γ-chain containing cytokine receptor (Hornakova et al., 2009). In fact, JAK mutation is

highly associated with aberrant cytokine receptor expression in B-ALL. Moreover, 70%

of B-ALL cases carrying a JAK mutation have concomitant deletion of IKZF1 and/or

CDKN2A/B. Patients with B-ALL associated with JAK mutation tend to have poor

outcome.

1.1.4 Epigenetic alterations in B-ALL

Aberrant microRNA expression

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression

at a posttranscriptional level and are involved in many biological processes, such as cell

proliferation and apoptosis. It has been shown that alterations in miRNA levels due to

genetic changes may be involved in leukemogenesis. For example, miRNA-125b1 (also

known as miR-125-1) was the first miRNA documented in B-ALL (Chapiro et al., 2010;

Sonoki et al., 2005). The gene encoding miR-125b1 is located at chromosome 11q24.1

but is inserted into rearranged IGH@ at chromosome 14q32 in rare patients with B-ALL.

The translocation causes overexpression of miR-125b1. MiR-125b1 is a negative

regulator of p53 (Le et al., 2009). The expression of miRNA are also characteristically

associated with genetic types of pediatric B-ALL and predict for clinical outcome

13

(Schotte et al., 2011). Discovery of novel miRNAs in B-ALL is still in progress, and the

clinical and biological significance of these miRNAs needs to be clarified.

DNA methylation

Our laboratory has extensively studied DNA methylation patterns in lymphoid

malignancies. Taylor and colleagues (2007) identified 262 unique methylated CpG island

(CGI) loci in ALL lymphoblasts utilizing CGI microarray technology. By examining the

relationship between methylation and expression for 10 genes (DCC, DLC-1, DDX51,

KCNK2, LRP1B, NKX6-1, NOPE, PCDHGA12, RPIB9, ABCB1, and SLC2A14) cell

culture treatments were conducted with 5-aza-2-deoxycytidine and trichostatin A

followed by subsequent reverse transcription polymerase chain reaction (RT-PCR)

analysis. More than a 10 fold increase in mRNA expression was observed for two

previously identified tumor suppressor genes (DLC-1 and DCC) and also for RPIB9 and

PCDHGA12 genes after treating cells with demethylation agents. Bisulfite sequencing of

the promoter of RPIB9 indicated that expression might be inhibited by methylation within

SP1 and AP2 transcription factor binding motifs (Taylor et al., 2007). This study was

expanded by Burmeister and colleagues (2015) by investigating methylation status of six

regions spanning the CpG island in the promoter region of RUNDC3B in cancer cell

lines. Lymphoid malignancies were found to have higher methylation level and did not

express RUNDC3B compared with myeloid malignancies and solid tumors, supporting

the potential use of DNA methylation in this region as a biomarker for lymphoid

malignancies (Burmeister et al., 2017).

14

To elucidate the role of DNA methylation during B-cell development, genome-

wide DNA methylation analysis was also performed in our laboratory. The DNA

methylation status of pro-B, pre-BI, pre-BII, and naïve-B-cells was determined using the

methylated CpG island recovery assay followed by NGS. An overall decrease in

methylation was observed during the transition from pro-B to pre-BI, whereas no

differential methylation was observed in the pre-BI to pre-BII transition or in the pre-BII

to naïve B-cell transition (Almamun et al., 2014). Furthermore, integrated methylome and

transcriptome analysis was conducted to determine novel regulatory elements for

pediatric B-ALL patients. Aberrant promoter methylation was associated with the altered

expression of genes involved in transcriptional regulation, apoptosis, and proliferation.

Novel enhancer-like sequences were identified within intronic and intergenic

differentially methylated regions (DMRs). Aberrant methylation in these regions was

associated with the altered expression of neighboring genes involved in cell cycle

processes, lymphocyte activation and apoptosis. These genes include potential epi-driver

genes, such as SYNE1, PTPRS, PAWR, HDAC9, RGCC, MCOLN2, LYN, TRAF3, FLT1,

and MELK, which may provide a selective advantage to leukemic cells (Almamun et al.,

2015). Finally, the impact of aberrant intergenic DNA methylation on gene expression

was investigated in B-ALL patients. 84% of differentially methylated intergenic loci,

determined for B-ALL patients, were also bound by TFs known to play roles in

differentiation and B-cell development in a lymphoblastoid cell line. Further, an overall

downregulation of enhancer RNA (eRNA) transcripts was observed in pre-B ALL

15

patients and these transcripts were associated with the downregulation of putative target

genes involved in B-cell migration, proliferation, and apoptosis. The identification of

novel putative regulatory regions highlights the significance of intergenic DNA

sequences and may contribute to the identification of new therapeutic targets for the

treatment of B-ALL patients in the future (Almamun et al., 2017).

Other research groups have also investigated a role of DNA methylation in B-

ALL pathogenesis. Examining DNA methylation patterns in 69 pediatric B-ALL and 42

control samples Chatterton and colleagues report 325 genes that were hypermethylated

and down regulated, and 45 genes that were hypomethylated and upregulated across all

B-ALL samples, regardless of subtype (Chatterton et al., 2012). Furthermore, functional

annotation of these epigenetically deregulated genes underlined the role of genes

involved in cell signaling, cellular development, cell survival and apoptosis. Another

study investigating 764 cases of newly diagnosed ALL and 27 cases of relapse,

determined 9406 hypermethylated CpG sites with each cytogenetic subtype portraying a

unique set of hyper- and hypomethylated sites (Nordlund et al., 2013). These

differentially hypermethylated CpG sites were enriched for genes such as NANOG,

OCT4, SOX2, and REST. MLL-rearranged infant leukemia is one specific ALL subtype

that has been shown to display distinct promoter hypermethylation (Schafer et al., 2010).

Stumpel and colleagues identified a distinct DNA methylation pattern dependent on the

presence and type of MLL-fusion partner in a cohort of 57 newly diagnosed infant ALL

patients (Stumpel et al., 2009). In addition, the level of hypermethylation appeared to

16

correlate with a higher risk of relapse among infants carrying t(4;11) or t(11;19)

translocations. In another study of 5 MLL-rearranged infant ALL samples, genes

involved in oncogenesis and tumor progression (DAPK1, CCR6, HRK, LIFR, and FHIT)

were differentially methylated suggesting a role in the leukemogenesis of MLL-

rearranged ALL (Schafer et al., 2010).

Histone modification

Mutations in epigenetic modifying genes can result in a gain or loss of function of

key genes known to regulate histone marks. Jaffe and colleagues have used global

chromatin profiling and mass spectrometry to measure levels of histone modifications on

bulk chromatin in pediatric ALL cell lines (Jaffe et al., 2013). A novel cluster of cell lines

with a specific epigenetic signature was determined and increased dimethylation of

histone H3 at lysine 36 (H3K36me2) and decreased unmodified H3K36 have been

observed. Approximately half of the cell lines in this cluster harbored the t(4;14)

translocation, which can contribute to NSD2 overexpression (Malgeri et al., 2000). NSD2

is a member of the HKMTs that catalyze the conversion of unmodified H3K36 to mono-

and dimethylated states (Kuo et al., 2011). NSD2 mutations were found to be enriched in

ETV6-RUNX1 and TCF3-PBX1 sub-types of pediatric B-ALL, while no mutations were

identified in 30 adult ALL samples. These were gain-of function mutations and their

overexpression led to a global increase in H3K36me2, with subsequent decrease in

H3K27me3. These results show that NSD2 mutation may affect expression of a number

of genes involved in normal lymphoid development.

17

In order to identify novel mutations in relapsed ALL, Mullighan and colleagues

performed targeted resequencing of 300 genes in 23 matched relapse-diagnosis B-ALL

pairs (Mullighan et al., 2011).The authors determined novel mutations in CREBBP, a

gene encoding the transcriptional coactivator CREB binding protein with histone

acetyltransferase activity. The overall frequencies of these mutations were 18.3% in

relapse cases. However, high incidences of somatic CREBBP alterations (63%) were

found in the high hyperdiploidy relapse cases. The majority of these mutations occurred

in the HAT domain (Inthal et al., 2012). Mutations in other important epigenetic

regulators such as NCOR1 (nuclear corepressor complex), EP300 (a paralog of

CREBBP), EZH2 (histone methyltransferase gene), and CTCF (zinc finger protein

involved in histone modifications) were less frequently observed (Mullighan et al., 2011).

Additionally, transcriptome sequencing has identified relapse-specific mutations in CBX3

(encoding heterochromatin protein), PRMT2 (gene encoding protein arginine

methyltransferase 2), and MIER3 (involved in chromatin binding); providing further

evidence of aberrant epigenetic mechanisms that play a role at relapse (Meyer et al.,

2013).

1.2 Alternative Splicing in B-ALL

1.2.1 Characteristics of Alternative Splicing Events in Cancer

Alternative splicing generates numerous protein isoforms through modifying

mRNA precursors. This mechanism is highly regulated under normal conditions in order

to generate proteomic diversity sufficient for the functional requirements of complex

18

tissues. While corrupted, cancer cells take advantage of this mechanism to generate

abnormal proteins with added, deleted, or altered functional domains that contribute to

carcinogenesis (Zhang & Manley, 2013). Cancer-specific alternative splicing includes all

of the five main alternative splicing patterns observed in normal tissues: cassette exons,

alternative 5′ splice sites, alternative 3′ splice sites, intron retention, and mutually

exclusive exons. The most prevalent pattern is the cassette-type alternative exon,

including skipping of one exon, skipping of multiple exons and/or exon inclusion. This

alteration results into truncated RNA transcript that may not be translated into functional

protein. Additionally, crucial protein domains may be excluded from protein structure

that will lead to the inability to interact with variety of protein partners and the

deregulation of signaling pathways. Alternative selection of 5′ or 3′ splice sites within

exon sequences may lead to subtle changes in the coding sequence, and an additional

layer of complexity arises with mutually exclusive alternative exons (Wang et al., 2015).

Both mechanisms may lead to alteration of amino acid composition of the protein and an

inability to perform its original function. Intron retention is positioned primarily in the

untranslated regions (UTRs) (Galante, Sakabe, Kirschbaum-Slager, & de Souza, 2004)

and has been associated with weaker splice sites, short intron length and the regulation of

cis-regulatory elements (Sakabe & de Souza, 2007). Complex splicing patterns may

affect gene expression as well and contribute to the diversity of protein isoforms. Specific

examples for each of these alterations are described in Table 1.

19

1.2.2 Alternative splicing isoforms in B-ALL

There are several studies that investigated alternatively spliced (AS) transcripts in

B-ALL. A transcript variant of Beclin 1 gene carrying a deletion of exon 11 has been

discovered in human B-cell acute lymphoblastic leukemia cells (Niu et al., 2014). The

alternative isoform was assessed by bioinformatics, immunoblotting and subcellular

localization. The results showed that this variable transcript is generated by alternative 3'

splicing, and its translational product displayed a reduced activity in induction of

autophagy by starvation, indicating that the spliced isoform might function as a dominant

negative modulator of autophagy and might play important roles in leukemogenesis.

In another study, expression levels of IKAROS have been measured in human

bone marrow samples from patients with adult acute lymphoblastic leukemia (Nakase et

al., 2000). Overexpression of the dominant negative isoform of IKAROS gene IK-6 was

observed in 14 of 41 B-cell ALL patients by RT-PCR, and the results were confirmed by

sequencing analysis and immunoblotting. Southern blotting analysis with PstI digestion

revealed that those patients with the dominant negative isoform IK-6 might have small

mutations in the IKAROS locus that may contribute to B-ALL through the dominant

negative isoform IK-6.

Different AS variants of activation-induced cytidine deaminase (AID) gene have

been identified among 61 adult BCR-ABL1+ ALL patients (Iacobucci et al., 2010). AID

expression was detected in 36 patients (59%); it correlated with the BCR-ABL1 transcript

levels and disappeared after treatment with tyrosine kinase inhibitors. Different AID

20

splice variants were identified: full-length isoform; AIDΔE4a, with a 30-bp deletion of

exon 4; AIDΔE4, with exon 4 deletion; AIDins3, with the retention of intron 3; AIDΔE3-

E4 isoform without deaminase activity. AID expression correlated with a higher number

of copy number alterations identified in genome-wide analysis using a single-nucleotide

polymorphism array. However, the expression of AID at diagnosis was not associated

with a worse prognosis.

Alternative PAX5 splicing was observed in 49 out of 100 ALL patients, which

comprises 62% of adult and 36% of pediatric ALL cases (Santoro et al., 2009). Different

isoforms were detected: PAX5D2 was found in 29 patients, PAX5D8–9 in 14 patients;

the novel PAX5D5 isoform was documented in six patients. These results suggests that

that altered PAX5 isoform expression may be involved in ALL pathogenesis.

1.3 Rationale for Thesis

To extend the integrated methylome and transcriptome analysis for B-ALL

patients reported by Almamun and colleagues (2015), sixteen RNA-seq samples (eight B-

ALL patients and eight healthy donors) have been analyzed with the edgeR package for

the purpose of obtaining a set of statistically significant transcripts that are differentially

expressed between these conditions. Some of the patient samples were excluded from

analysis due to high proportion of reads aligned to 5’UTR region (around 80%) reducing

the patient sample number to 8. Therefore, this analysis utilized an equal number of

patient and control samples improving statistical robustness and providing increased

power in determining the differences in variances and means for DE genes.

21

The Bioconductor package edgeR was utilized to identify DE transcripts due to its

advantages over Cuffdiff. For example, edgeR normalizes RNA-seq data according to

library size (trimmed mean of M-values, TMM method), while Cuffdiff software

normalizes data according to previously annotated genes and their gene coordinates

(fragments per kilobase of transcript per million mapped reads, FPKM method). In

addition, edgeR and Cuffdiff differ in the calculation of mean and variance of gene

expression values. The negative binomial model, implemented into Cuffdiff, assumes that

there is no relationship between mean and variance of gene expression values in

experimental and control groups. Contrarily, the edgeR algorithm “borrows” information

about variances across multiple genes that undergo statistical testing, making this model

more robust in determining a set of DE transcripts. Moreover, edgeR implements several

modalities to perform statistical test depending on experimental design: the classic edgeR

model utilizes Fisher’s exact test for pairwaise comparisons, while the generalized linear

model (GLM) is more suitable for multigroup experiments. Further, edgeR comprises of

wide range of graphic functions that allow the researcher to visualize and plot RNA-seq

data in addition to performing statistical tests, such as multidimensional scaling plots

(MDS plot) or volcano plots. Finally, R code can be utilized to modify the functions in

the edgeR package according to experimental demands. In sum, edgeR analysis has

multiple advantages in comparison to Cuffdiff analysis and provides a superior analysis

of transcriptome data. Currently, it is one of the best methodologies for RNA-Seq data

analysis along with DESeq analysis.

22

It has been previously shown that alternative splicing is a hallmark of a variety of

malignancies, including both solid and soft tissues cancers (Table 1). Prior to NGS,

transcriptome-wide analysis of AS genes was limited due to the inability to generate

primers (or hybridization probes) for regions, where novel alternative transcripts may be

located. Currently, RNA-seq technology allows one to investigate not only differentially

expressed (DE) genes across multiple groups, but also provides information about

disease-specific gene isoforms. To identify a set of differentially spliced variants

common across B-ALL patients, a custom Perl script was designed. This information

may shed light on the functional implication of AS isoforms that may be involved in B-

ALL pathogenesis.

Finally, to explore the potential role of DNA methylation in transcriptional

regulation, an in vitro model for B-ALL – Nalm 6 cell line – was utilized. Prior to this

study, Taylor and colleagues (2007) examined the relationship between methylation and

expression for 10 genes using CpG island microarrays and observed more than a 10 fold

increase in mRNA expression for two tumor suppressor genes (DLC-1 and DCC) and

also for RPIB9 and PCDHGA12 genes. In this study, the Nalm 6 cell line was treated

with a demethylating agent followed by NGS analysis. Although in vitro models may not

reflect the whole complexity of patient transcriptomes, they provide a means to explore

potential functional mechanisms responsible for the aberrant transcript expression

identified in our computational analysis.

23

1.4 Experimental Aims and Hypothesis

We hypothesize that DE genes, identified between B-ALL patients and healthy

donors, are involved in the development and progression of this malignancy. Further, we

hypothesize that leukemic cells will have unique splicing alterations that result in

abnormal transcripts which promote the survival and uncontrolled proliferation of

malignant cells. Our goal is to conduct genome-wide transcriptome analysis to identify a

set of differentially expressed and spliced genes between B-ALL patients and healthy

donors and to investigate the functional implications of these alterations using network-

based analysis. In addition, a mechanistic study utilizing the Nalm 6 cell line was

performed to explore if methylation influences alternative splicing of transcripts in B-

ALL. To address our hypotheses the following project objectives were completed:

1. Perform edgeR analysis between B-ALL and healthy donor samples to determine a set

of statistically significant DE genes.

2. Utilize Ingenuity Knowledge Base (KB) to annotate functions and enrichment of

signaling pathways for DE genes.

3. Identify novel transcriptional regulators that control aberrant expression of genes

involved in the development of B-ALL using Ingenuity pathway analysis (IPA).

4. Determine a set of common splicing isoforms for B-ALL patients using custom Perl

script.

5. Perform a mechanistic study in the Nalm 6 cell line to explore the impact of DNA

methylation upon common AS isoforms.

24

The complexity of a disease such as B-ALL provides many difficulties to

determining diagnosis, prognosis, and appropriate treatment. To date a number of genetic

abnormalities have been identified that contribute to B-ALL development but there are

still many to be characterized. Therefore, a complete transcriptome analysis to identify

DE genes is very important for better understanding B-ALL pathobiology. This research

provides a characterization of aberrant gene expression patterns in B-ALL at the whole

transcriptome scale in an attempt to improve diagnosis, prognostication and treatment of

B-ALL patients in the future.

25

Chapter 2 RNA-Sequencing Analysis in B-cell Acute Lymphoblastic Leukemia Reveals

Aberrant Gene Expression and Splicing Alterations

Abstract

Background: B-cell acute lymphoblastic leukemia (B-ALL) is a neoplasm of immature

lymphoid progenitors and is the leading cause of cancer-related death in children. The

majority of B-ALL cases are characterized by recurring structural chromosomal

rearrangements that are crucial for triggering leukemogenesis, but do not explain all

incidences of disease. Therefore, other molecular mechanisms, such as alternative

splicing and epigenetic regulation may alter expression of transcripts that are associated

with the development of B-ALL. To determine differentially expressed and spliced RNA

transcripts in precursor B-cell acute lymphoblastic leukemia patients a high throughput

RNA-seq analysis was performed.

Methods: Eight B-ALL patients and eight healthy donors were analyzed by RNA-seq

analysis. Statistical testing was performed in edgeR. Each annotated gene was mapped to

its corresponding gene object in the Ingenuity KB. Analysis of RNA-seq data for splicing

alterations in B-ALL patients and healthy donors was performed with custom Perl script.

Results: Using edgeR analysis, 3877 DE genes between B-ALL patients and healthy

donors based on TMM (trimmed mean of M-values) normalization method and false

discovery rate, FDR < 0.01, logarithmically transformed fold changes, logFC > 2) were

identified. IPA revealed abnormal activation of ERBB2, TGFB1 and IL2 transcriptional

factors that are crucial for maintaining proliferation and survival potential of leukemic

26

cells. B-ALL specific isoforms were observed for genes with roles in important canonical

signaling pathways, such as oxidative phosphorylation and mitochondrial dysfunction. A

mechanistic study with the Nalm 6 cell line revealed that some of these gene isoforms

significantly change their expression upon 5-Aza treatment, suggesting that they may be

epigenetically regulated in B-ALL.

Conclusion: Our data provide new insights and perspectives on the regulation of the

transcriptome in B-ALL. In addition, we identified transcript isoforms and pathways that

may play key roles in the pathogenesis of B-ALL. These results further our understanding

of the transcriptional regulation associated with B-ALL development and will contribute

to the development of novel strategies aimed towards improving diagnosis and managing

patients with B-ALL.

Keywords: B-ALL, RNA-sequencing, differential gene expression, alternative splicing

Introduction

B-cell precursor acute lymphoblastic leukemia (B-ALL), a malignant disease of

lymphoid progenitor cells, affects both children and adults, with peak prevalence between

the ages of 2 and 5 years (Pui et al., 2008). A number of genetic alterations have been

determined in B-ALL (Woo, Alberti, & Tirado, 2014); however, a complete

understanding of pathogenic mechanisms underlying B-ALL development is still lacking.

To identify genetic alterations in B-ALL, a wide range of methods have been applied

including cytogenetic analysis (Mrózek, Harper, & Aplan, 2009), array comparative

genomic hybridization (Dawson et al., 2011) and recently whole exome sequencing

27

(Lilljebjörn et al., 2012). The whole exome sequencing of B-ALL samples has also

resulted in the identification of novel recurring mutations in NRAS, KRAS, FLT3,

CREBBP, XBP1, WHSC1, and UBA2 genes (Griffith et al., 2016; Lilljebjörn et al., 2012).

To study the whole transcriptome of cells, microarrays have been extensively

used, and these studies have determined a number of DE genes (Ross et al., 2003).

Unfortunately, microarray techniques have a number of limitations including, cross

hybridization of transcripts, limitation in coverage, inability to resolve novel transcripts

and falsely higher estimation of low abundance transcripts (Pawitan, Michiels, Koscielny,

Gusnanto, & Ploner, 2005). With the development of massive parallel RNA-sequencing

(RNA-seq) technology, there have been a growing number of genome-wide studies that

have analyzed the complete transcriptome of cells in different malignancies (Eswaran et

al., 2012), and non-malignant diseases (Twine, Janitz, Wilkins, & Janitz, 2011). Besides

analyzing the expression level of genes, RNA-seq technology has the added advantage of

analyzing expression at the exon level and provides detailed information about alternative

splicing variations, novel transcripts, fusion genes, differential transcriptional start sites

and genomic mutations (Wang et al., 2008). As all the RNA transcripts are being directly

sequenced, this technology is ideally suited to study altered splicing patterns which is

especially relevant in cancer cells (David & Manley, 2010).

In this study we performed RNA-seq analysis on B-ALL patient samples and

healthy donor samples to determine transcriptome differences and splicing variations. A

number of DE genes and novel isoforms were identified. These findings may facilitate

28

the identification of novel prognostic markers, therapeutic targets and altered signaling

pathways in B-ALL.

Materials and Methods

Sample isolation and characterization

De-identified patient samples were obtained under full ethical approval of the

Institutional Review Board at the University of Missouri. A total of 8 pre-B ALL patient

samples were used for this study (Table 2). ALL patient samples contain at least 88%

blasts. The age of patients varied between 17 month and 15 years. The blast cells were

positive for CD19 and CD10 markers. A half of the B-ALL patients have normal

karyotype and the rest of the patients have multiple chromosome abnormalities, including

deletions, translocations and presence of derivative chromosome. Patient A19 had been

identified with hyperdiploid genotype. Normal control pre-BI and pre-BII cells were

isolated from 8 human umbilical cord blood samples as previously described (Almamun

et al., 2013) and served as the control group. Briefly, mononuclear cells were isolated by

density gradient centrifugation using Ficoll-Paque PLUS (GE Healthcare Bio-Sciences

AB; cat. no. 17-1440-03) followed by depletion of all non B-cells with biotin conjugated

antibodies cocktail and anti-biotin monoclonal antibodies conjugated to magnetic beads

using human B cell Isolation Kit (MACS Miltenyi Biotec; order no. 130-093-660).

Finally, the fluorescently labeled cells were sorted as pre-BI (CD19+/CD34-/CD45low)

and pre-BII (CD19+/CD34-/CD45med). Transcriptomes were generated for precursor B-

cells which include both pre-BI and pre-BII subsets. To obtain this population of cells,

29

purified B-cells were fluorescently labeled with antibodies against CD19 and IgM and

precursor B-cells (CD19+/IgM-) were isolated by flow cytometry (Almamun et al., 2015).

RNA-seq and library preparation

RNA samples were also obtained from the pre-B ALL patients (8 samples) and

from normal precursor B-cells isolated from HCB (8 samples). RNA sequencing libraries

were constructed with the NEBNext® UltraTM Directional RNA Library Prep Kit for

Illumina® (New England Biolabs; cat. no. E7420) and sequenced on the Illumina HiSeq

2000 (1˟100 bp reads) at the University of Missouri DNA Core Facility. All RNA-seq

data were deposited in NCBI Sequence Read Archive (Accession SRP058414).

(Almamun et al., 2015).

Primary processing and mapping of RNA-seq reads

100 bp single-end RNA-seq reads were obtained from Illumina HiSeq 2000

sequencing platform. Raw data files were generated in FASTQ format and adaptor

sequences had been trimmed. RNA-seq data were processed using an in-house pipeline.

The Fred quality score of RNA-seq reads was obtained by using the FastX-Toolkit v.

0.0.13 and the mean value for Fred base calling was 32, indicating a good-quality call in

the 100 bp reads (Gordon and Hannon, unpublished). Reads were then processed and

aligned to the UCSC H. sapiens reference genome (build hg19) using TopHat v1.3.3

(Trapnell, Pachter, & Salzberg, 2009).

Assembly of transcripts and differential expression

30

The aligned read BAM files were assembled into transcripts, their abundance

estimated by Cufflinks v2.0.1 (Trapnell et al., 2012). Cufflinks uses the normalized

RNA-seq fragment counts to measure the relative abundances of transcripts. The unit of

measurement is fragments per kilobase of exon per million fragments mapped (FPKM).

Confidence intervals for FPKM estimates were calculated using a Bayesian inference

method. After assembly with Cufflinks, the output files were sent to Cuffmerge along

with a reference annotation file. To produce count tables for edgeR analysis, HTSeq

v0.6.1 software was utilized (Anders, Pul, & Huber, 2014). The count tables represent the

total number of reads aligning to each gene (or other genomic locus). To normalize

multiple samples for differential expression analysis, we applied calcNormFactors

function in edgeR to find a set of scaling factors for the library sizes that minimize the

log-fold changes between the samples for most genes. The default method for computing

these scale factors uses a trimmed mean of M-values (TMM) between each pair of

samples (Robinson & Oshlack, 2010). For cross-replicate dispersion estimation, a

quantile-adjusted conditional maximum likelihood (qCML) method was used to calculate

the likelihood by conditioning on the total counts for each tag, using pseudo counts after

adjusting for library sizes. qCML common dispersion and tagwise dispersions were

estimated using the estimateCommonDisp() and estimateTagwiseDisp() functions

(Robinson, McCarthy, & Smyth, 2010). The expression testing was done at the level of

transcripts and genes and pairwise comparisons of expression between B-ALL and

normal samples. Only the comparisons with p-value and FDR less than 0.01 and

31

expression fold change greater than two fold in the edgeR output were regarded as

showing significant differential expression.

Identification of common gene isoforms

To identify common gene isoforms for B-ALL patients, unique identifiers were

assigned to each isoform using a custom Perl script. Briefly, after alignment to the

reference hg19 human genome, each patient file was processed using the Cufflinks

program and individual transcriptomes were assembled into corresponding transcripts.gtf

files. Each transcripts.gtf file consists of eight columns: the first seven columns have

standard GTF format, and the last column contains attributes. To create a unique

identifier for each transcript the following information was extracted from transcripts.gtf

files: transcript ID, chromosome number and exon coordinates. Then, intron coordinates

were calculated for each transcript ID using a Perl script. Furthermore, chromosome

number and intron coordinates were merged into unique identifier (for example:

CUFF.59863.1 transcript has unique ID chr7:156629580-156685621:156626487-

156629506:156619439-156626446:156589187-156619298). Then, FPKM values were

extracted from the same transcripts.gtf files to obtain relative abundance for transcripts

with unique IDs. Finally, identified transcripts were annotated with corresponding genes.

PerlDBI module and MySQL quarries were utilized to obtain a set of common unique

transcripts with corresponding FPKM values. Overall, 338 common transcripts were

identified in B-ALL patients. The corresponding FPKM values were extracted with

further logarithmic transformation (base 2) and clustered using the R package

32

ComplexHeatmap (Gu et al., 2016). By agglomerative hierarchical cluster analysis,

Euclidian distances have been determined for each pair of transcripts and plotted as a

heatmap to visualize transcripts abundances for B-ALL patients.

Cell line treatment experiment

The pre-B ALL cell line Nalm 6 was grown in RPMI 1640 medium (Gibco®,

ThermoFisher) supplemented with 10% fetal bovine serum, L-glutamine, and gentamicin.

Cell culture treatments were conducted, as described previously with minor alterations

(Taylor et al., 2007). Briefly, Nalm 6 cells were seeded at 3 X 106 cells/mL. Based on

prior practice, 5-Aza was added at either a 0.3 or 0.4 μmol/L final concentration with

acetic acid as the vehicle and was incubated for 78 h, with new medium added every 24

h. Control cells were cultured with acetic acid alone. RNA from the cultured cells was

extracted for use in NGS, using the AllPrep DNA/RNA Mini Kit (QIAGEN). High

quality RNA was submitted to the University of Missouri DNA Core Facility for library

generation using the TruSeq mRNA stranded library preparation kit (Illumina). Paired-

end sequences (2 X 75) were generated by the University of Missouri DNA Core Facility

using the Illumina HiSeq 2500 platform. Sequence files were generated in FASTQ format

and processed as described for B-ALL patients and healthy donors.

Functional annotation of differentially expressed genes

QIAGEN’s Ingenuity Pathway Analysis (IPA®, QIAGEN Redwood City, CA

www.qiagen.com/ingenuity) is a powerful analysis and search tool that uncovers the

significance of omics data and identifies new targets or candidate biomarkers within the

33

context of biological systems. IPA was used to categorize genes that were differentially

expressed between B-ALL patients and healthy donors. The analysis was run using the

following setting in IPA: all defaults setting for the selection of dataset, 2 fold change

cutoff, FDR = 0.001 and p-value = 0.001.

The functional analysis in IPA identified the biological functions that were most

significant to the analyzed dataset. The significance value associated with functional

analysis for a dataset is a measure of the likelihood that the association between a set of

DE genes in our dataset and a given process or pathway is due to random chance. The

smaller the p-value the less likely that the association is random and the more significant

the association. In general, p-values less than 0.05 indicate a statistically significant, non-

random association. The p-value is calculated using the right-tailed Fisher exact test. In

this method, the p-value for a given function is calculated by considering a) the number

of DE genes that participate in that function and b) the total number of genes that are

known to be associated with that function in the Ingenuity KB. The more DE genes that

are involved, the more likely the association is not due to random chance, and thus the

more significant the p-value. Similarly, the larger the total number of DE genes known to

be associated with the process, the greater the likelihood that an association is due to

random chance, and the p-value accordingly becomes less significant. To sum up, the p-

value identifies statistically significant over-representation of DE genes in a given

process. Over-represented functional or pathway processes are processes which have

more focus molecules than expected by chance.

34

Canonical pathway analysis identified the pathways from the Ingenuity KB that

were most significant to the dataset. DE genes from the dataset that were associated with

a canonical pathway in the Ingenuity KB were considered for the analysis. The

significance of the association between the data set and the canonical pathway was

measured in 2 ways: 1) a ratio of the DE genes that mapped to the pathway divided by the

total number of genes that mapped to the canonical pathway; 2) an FDR ≤ 0.05 to

calculate a p-value determining the probability that the association between the DE genes

and the signaling canonical pathway was explained by chance alone. A simple p-value

was also considered and reported in the results.

The IPA upstream regulator analysis was also performed. This analysis is based

on prior knowledge of expected effects between transcriptional regulators and their target

genes stored in the Ingenuity KB. The analysis examines how many known targets of

each transcription regulator are present in the provided dataset, and also compares their

direction of change to what is expected from the literature in order to predict likely

relevant transcriptional regulators. If the observed direction of change is mostly

consistent with a particular activation state of the transcriptional regulator (“activated” or

“inhibited”), then a prediction is made about that activation state. IPA’s definition of

upstream transcriptional regulator is quite broad – any molecule that can affect the

expression of other molecules, which means that upstream regulators can be almost any

type of molecule, from TFs, to miRNAs, kinases, compound or drug.

35

For each potential transcriptional regulator (TR) two statistical measures, an

overlap p‐value and an activation z‐score are computed. The overlap p‐value calls likely

upstream regulators based on significant overlap between dataset genes and known

targets regulated by a transcriptional regulator. The activation z‐score is used to infer

likely activation states of upstream regulators based on comparison with a model that

assigns random regulation directions. The purpose of the overlap p‐value is to identify

transcriptional regulators that are able to explain observed gene expression changes. The

overlap p‐value measures whether there is a statistically significant overlap between the

dataset genes and the genes that are regulated by a transcriptional regulator. It is

calculated using Fisher’s exact test and significance is generally attributed to p‐values <

0.01. Since the regulation direction (“activating” or “inhibiting”) of an edge is not taken

into account for the computation of overlap p‐values the underlying network also

includes findings without associated directional attributes, such as protein‐DNA binding.

Results

Analysis of RNA-seq data

Normal precursor B-cells from 8 healthy donors (HCB11, HCB12, HCB13,

HCB15, HCB16, HCB17, HCB18 and HCB19) and malignant precursor B-cells from 8

B-ALL patients (B-ALL18, B-ALL19, B-ALL20, B-ALL23, B-ALL24, B-ALL26, B-

ALL30 and B-ALL36) were subjected to RNA single-end RNA-sequencing. The total

number of raw reads in healthy (n = 8) and B-ALL (n = 8) samples ranged from 27 to 52

million reads, and 25 to 51 million reads, respectively (Supplemental Tables 1 and 2). To

36

assess the quality of mapping reads to the reference genome hg19, some key metrics were

extracted from the TopHat2 output, and analyzed using the RNA-seq quality control

package RseQC (Wang, Wang, & Li, 2012). The majority of reads (between 76 % and

89.5 %) were uniquely mapped to the reference genome sequence across all samples

(Supplemental Tables 1 and 2). The mean mapping percentage for healthy donors and B-

ALL patients was 88.9 % and 85.8 %. In addition 2.5% to 4.0% of the reads mapped to

known splice junctions in healthy donors and B-ALL patients respectively (Supplemental

Tables 3 and 4).

To further examine the read distribution, the uniquely mapped reads were

assigned to: exon coding sequence (CDS), 5’ and 3’ untranslated regions (5’UTR and

3’UTR), introns and intergenic regions. In Figure 2, the distribution of mapped reads is

shown across the samples. 28.2 % to 55.0 % of reads mapped to exon coding sequence,

3.0 % to 7.1 % mapped to 5’UTR while 9 % to 19.5 % mapped to 3’UTR. The introns

and intergenic regions account for about 30.5 % and 10.1 %, respectively (Supplemental

Tables 5 and 6). To further visualize the read distribution percentages in healthy donors

and B-ALL patients, mapping data from Figure 2 was averaged and plotted as a pie chart

(Figure 3). The exonic reads (CDS) were higher in B-ALL patients (~51%) as compared

to healthy donors (~31%) while intronic reads were higher in the healthy donors (~43%),

compared to B-ALL patients (18%). The high number of reads mapping to introns have

been reported in other RNA-seq analysis (Kapranov et al., 2011) and could be due to

37

novel exons, or nascent transcription and co-transcriptional splicing as described by

Ameur and colleagues.

Analysis of differentially expressed genes

To determine the DE genes between B-ALL patients and healthy donors an edgeR

analysis was performed. For this purpose we used the “classic” edgeR model that

employs Fisher’s exact test for identifying DE genes. After filtering DE genes with a

FDR < 0.01, p-value < 0.01 and logFC > 2, there were 3877 DE genes between B-ALL

patients and healthy donors. Among these genes, 2601 were upregulated in B-ALL and

1276 genes were downregulated. The top twenty upregulated and twenty downregulated

genes are listed in Table 3.

Treatment of a pre-B ALL cell line with a demethylating agent reverses expression of

alternatively spliced isoforms in vitro

Because alternative isoform usage have been shown to be associated with aberrant

DNA methylation in cancer (Bujko et al., 2016), a pre-B ALL cell line Nalm 6 was

treated with a demethylating agent (5-aza-2'-deoxycytidine, 5-Aza) and RNA-seq was

performed. Differential gene expression was calculated between Nalm 6 samples and

healthy donor’s samples using edgeR package for each of the 338 common transcripts in

B-ALL patients identified by custom Perl script (see section “analysis of differentially

expressed genes”). Three pairwise comparisons have been examined: B-ALL versus

healthy donors, Nalm 6 (untreated) versus healthy donors and Nalm6 (treated with 5-

38

Aza) versus healthy donors. Nalm 6 cells treated with 5-aza have higher expression

values in comparison to untreated Nalm 6 cells, as expected after treatment with

demethylating agent. While analyzing expression values for 338 common transcripts, 295

transcripts have been identified in all three pairwise comparisons, 275 transcripts among

them met criteria p-value < 0.05 and 78 common transcripts among them have logFC > 2.

Interestingly, we identified 19 common transcripts that have shown significant increase in

expression after 5-Aza treatment (Table 4). The associated gene ontology terms for these

genes are presented in Table 5.

Functional pathway analysis

Several top bio functions were identified by IPA, including cellular growth and

proliferation (1.65E-05 - 8.80E-28), cell death and survival (1.34E-05 - 6.55E-21),

cellular movement (1.47E-05 - 5.00E-20), cellular development (1.65E-05 - 6.55E-18)

and cell cycle (1.63E-05 - 9.22E-13). The cellular growth and proliferation category

describes functions associated with cell expansion and propagation, such as proliferation

and outgrowth of cells. This category included 1351 genes, including syndecan 2 (SDC2),

CD2 molecule (CD2), MAM domain-containing protein 1 (MDGA2) and Wnt Family

Member 10A (WNT10A). The cellular development category describes functions

associated with the development and differentiation of cells, including maturation and

senescence of cells. This category consisted of 1164 genes, including neuritin 1 (NRN1),

kinesin family member 26A (KIF26A), intelectin 1 (ITLN1) and uroplakin 2 (UPK2)

genes. The cell death and survival category (represented by 1155 genes including

39

baculoviral IAP repeat containing 7 (BIRC7), Fc fragment of IgG receptor IIIa

(FCGR3A), calcium/calmodulin dependent protein kinase II alpha (CAMK2A) and

nephrin (NPHS1)) describes functions associated with cellular death and survival, such as

cytolysis, necrosis, apoptosis and recovery of cells. The cellular movement category

(represented by 812 genes, including prostaglandin D2 receptor (PTGDR), semaphorin

3F (SEMA3F) and natriuretic peptide B (NPPB)) describes functions associated with

movement and localization of cells, including chemotaxis, infiltration, rearrangement,

and transmigration of cells. These functions were primarily up-regulated among B-ALL

patients.

The IPA software reported several significant canonical pathways, including

protein kinase A signaling (p-value ≤ 1.55E-06), interferon signaling (p-value ≤ 3.26E-

03), cyclins and cell cycle regulation (p-value ≤ 2.20E-03), phospholipase C signaling (p-

value ≤ 1.56-E03) and cell cycle control of chromosomal replication (p-value ≤ 4.39E-

05). The result from this part of functional analysis is reported in Table 6. In addition,

identified common gene isoforms for B-ALL patients associated with oxidative

phosphorylation (p-value ≤ 4.58E-13) and mitochondrial dysfunction pathways (p-value

≤ 4.04E-11).

The upstream regulatory analysis performed by IPA predicted regulators based on

the consistency of expression direction changes for DE genes within each pathway. The

most important regulators identified in this analysis were Erb-B2 receptor tyrosine kinase

2 (ERBB2), transforming growth factor beta 1 (TGFB1) (Figure 5), interleukin-2 (IL2),

40

tumor protein P53 (TP53) and cyclin dependent kinase inhibitor 1A (CDKN1A).

ERBB2, TGFB1 and IL2 were predicted to be activated in B-ALL group. For TP53 and

CDKN1A it was not possible to infer their activation or inactivation based upon DE gene

set.

Discussion

On average, more than 38 million unique mapped RNA-seq reads were generated

providing genome-wide coverage of the transcriptome in eight pediatric B-ALL patients.

Importantly, these profiles were compared to healthy precursor B-cells isolated from

umbilical cord blood, the normal counterparts of malignant precursor B-cells to identify

DE genes. Previous studies in B-ALL have shown an inverse correlation between DNA

methylation and gene expression in CpG islands and gene promotors (Busche et al.,

2013); however more than 80% of DMRs are located in intronic or intergenic regions

(Almamun et al, 2015). The novelty of our study is to investigate how DNA methylation

affects alternatively expressed and spliced transcripts unique to B-ALL patients. Since

DNA methylation can be used as a biomarkers and as a target for novel therapeutics, we

sought to identify B-ALL specific alternate transcript candidates that were the most likely

to be regulated by DNA methylation.

The edgeR analysis identified DE genes involved in immune regulation and

provide survival advantage to cancer cells. For example, a member of the IAP family of

apoptosis inhibitors BIRC7 was top upregulated gene in B-ALL group. This gene had

also been overexpressed 25-fold in ETV6-RUNX1 (also known as TEL-AML1) leukemia

41

(Ross et al., 2003). The top downregulated gene in B-ALL group – CAMK2A has been

identified as distinctive protein kinase gene at ALL1/AF4 subgroup of adult B-cell acute

lymphoblastic leukemia patients (Messina et al., 2010). The product of this gene belongs

to the serine/threonine protein kinases family and is involved in calcium signaling.

Several novel upregulated genes, including FAM19A5 (chemokine regulation), PTGDR

(prostaglandin D receptor activity), GIMAP6 (regulation of cell survival), FCN1 (antigen

binding activities) and GZMA (regulator of apoptosis) also involved in regulation of

immune system and cell death. Interestingly, TSHZ3 gene may play role in epigenetic

regulation, because TSHZ3-mediated transcription repression involves the recruitment of

histone deacetylases HDAC1 and HDAC2. Furthermore, several novel downregulated

genes have been annotated with immune response and signal transduction categories,

including ITLN1 (IL-7 signaling pathway regulator), CD244 (adaptive immune response

regulator) and ORM1 (immunosuppression process). Moreover, downregulation of TNS4

gene may disrupt the link between signal transduction pathways and cytoskeleton, which

results into apoptosis inhibition. Taken together, these genes may contribute to the

immune dysfunction of B-cells and disrupt proper differentiation of B-cells.

Many of biological functions reported by IPA are likely related to the malignant

phenotype of cancer cells. The top functional category – cellular growth and proliferation

had been comprised of 1351 DE gene, which highlight abnormal propagation of leukemic

cells. The cell transformation category (Figure 6) involved upregulated genes, such as

CD4 (regulator of N-RAS pathway), E2F1 (control of cell cycle), MYB (proto-oncogene),

42

RUNX1 (enhancer activity), VEGFA (growth factor activity), AURKA and AURKB

(kinase activity) and downregulated genes HES1 (transcription factor activity) and IRF4

(regulator of B-cell receptor pathway). Similarly, proliferation of cancer cells (Figure 7)

involved upregulated genes, such as BIRC5 (negative regulator of apoptosis), CXCL8

(angiogenic factor), IL1B (cell differentiation regulator), NOTCH1 (transcription factor

activity) and downregulated IL6 (regulator of B-cell maturation) and IFNG (cytokine

activity) genes. In summary, the B-ALL expression profiles included the upregulation of

genes involved in cell proliferation and the downregulation of genes involved in B-cell

maturation.

The upstream regulatory analysis performed by IPA, which seeks to identify the

upstream transcriptional regulatory cascades that are likely to elucidate the observed

changes in gene expression may shed some light on the biological activities that occur in

leukemic cells. This analysis predicted the top upstream regulators to include TGFB1

which was predicted to be activated in B-ALL group (Figure 5). The transforming growth

factor-β (TGF-β) signaling pathway is an essential regulator of cellular processes,

including proliferation, differentiation, migration, and cell survival. During

hematopoiesis, the TGF-β signaling pathway is a potent negative regulator of

proliferation while stimulating differentiation and apoptosis when appropriate. However,

in hematologic malignancies, including leukemias, resistance to the homeostatic effects

of TGF-β develops. Mechanisms for this resistance include mutation or deletion of

43

members of the TGF-β signaling pathway and disruption of the pathway by oncoproteins

(Dong & Blobe, 2006).

Protein kinase A signaling was the top canonical pathway based on DE genes

between B-ALL patients and healthy donors. Protein kinase A (PKA), as cAMP-

dependent protein kinase, mediates signal transduction of G-protein coupled receptors

through its activation upon cAMP binding. It is involved in the control of a wide variety

of cellular processes from metabolism to ion channel activation, cell growth and

differentiation, gene expression and apoptosis. Importantly, since it has been implicated

in the initiation and progression of many tumors, PKA has been proposed as a novel

biomarker for cancer detection, and as a potential molecular target for cancer therapy

(Sapio et al., 2014).

The process of generating novel cancer-specific isoforms leads to structural

changes in coding regions and consequently, alter functionality of the resulting proteins.

It is crucial to distinguish isoforms that are generated due to natural transcriptomic

dynamics from the ones that occur in malignant cells. Perhaps the most intriguing finding

of this study was the identification of common AS transcripts for the B-ALL cohort. By

custom Perl script we elucidate 338 common gene isoforms that may play role in

oxidative phosphorylation and mitochondrial dysfunction pathways. Cancer cells prefer

glycolysis over oxidative phosphorylation to fulfill their energy demand, suggesting that

they have adapted to survive and proliferate in the absence of fully functional

mitochondria. In addition to that, dysfunctional mitochondria cannot neutralize effect

44

from reactive forms of oxygen (ROS), which may lead to oxidative stress inside cells and

alter crucial cellular processes, including regulation of gene transcription and alternative

splicing. Thus, leukemic cells may generate abnormal proteins with added, deleted, or

altered functional domains that contribute to pathogenesis of B-ALL.

Furthermore, the mechanistic study utilizing the Nalm 6 cell line revealed that

nineteen common gene isoforms significantly change their expression level after 5-Aza

treatment. Five genes among them – TK1, SNN, PLCG2, CYTIP and SDF2L1 – showed

consistent gene expression patterns in both comparisons: B-ALL versus healthy donors

and Nalm 6 versus healthy donors. TK1, SNN, PLCG2 and CYTIP genes were

downregulated in B-ALL and Nalm 6 groups, while SDF2L1 gene was upregulated.

Interestingly, SNN downregulation has been shown in monocytic cell populations in

chronic lymphocytic leukemia patients (Maffei et al., 2013) and might be regulated by

the TNFα-PKCε signaling pathway, which implies a role for SNN in cell death and cell

cycle regulation (Billingsley et al., 2006). In addition, downregulation of the PLCG2

gene may alter B-cell receptor signaling and lead to the disruption of the B-cell

maturation process (Ramsay & Rodriguez-Justo, 2013). Surprisingly, we do not observe

upregulation of TK1 gene, which is a well-known marker for ALL patient response to

therapy and reflects the aggressiveness of leukemic cells (O'Neill, Zhang, Li, Fuja, &

Murray, 2007). CYTIP upregulation was also reported in metastatic renal cancer

(Vanharanta et al., 2013), but it is not consistent with our findings for B-ALL patients. To

45

sum up, a mechanistic study with the Nalm 6 cell line, suggests that some of the common

gene isoforms may undergo epigenetic regulation in B-ALL.

Conclusions

The main strength of RNA sequencing data is that besides providing expression

analysis it can be further mined for a number of other genetic abnormalities, including

splicing alterations, fusion transcripts, alternate transcription start sites, point mutations,

novel transcripts, fusion genes that will provide novel insights in B-ALL. Our data

provide new insides and perspectives on the transcriptome regulation in B-ALL. We

identified transcript isoforms and pathways that play key role in pathogenesis of B-ALL.

These results improve our understanding of the transcriptional regulation underplaying B-

ALL development and will help develop strategies for better diagnosis and managing

patients with B-ALL in the future.

46

Major transcriptional

factors

B-cell development

stages

Immunophenotype

Figure 1. Schematic diagram of B-cell development stages, immunophenotype and major transcription factors (from Zhou et al. with

changes, 2008). HSC – hematopoietic stem cell, LMPP – lymphoid multipotent progenitor, CLP – common lymphoid progenitor.

47

Figure 2. Bar diagram represents distribution of uniquely mapped reads to human

genome UCSC hg19 (GRCh37). Each bar depicts the percentage of reads from individual

samples (8 B-ALL patients and 8 healthy donors) mapped to coding sequence exon

(CDS), 5’ and 3’ untranslated regions (5’ and 3’UTR), introns and intergenic regions.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CDS 5'UTR 3'UTR Introns Intergenic regions

48

Figure 3. Average percentage of sequencing reads from 8 B-ALL (top) and 8 healthy

donors (bottom) that map to coding sequence exon (CDS), 5’ and 3’ untranslated regions

(5’ and 3’UTR), introns and intergenic regions.

51%

4%

17%

18%

10%


31%

6%

10%

43%

10%


49

Figure 4. The heatmap representing common gene isoforms for B-ALL patients

identified by custom Perl script. The heatmap representing common gene isoforms for B-

ALL patients identified by custom Perl script. High-abundance transcripts in B-ALL

patients represented in red. Low-abundance transcripts in B-ALL patients represented in

blue. The intensity of color is related to level of transcripts abundances.

50

Figure 5. The mechanistic network of the inferred upstream regulator TGFB1. Genes

presented in red are related to genes that up-regulated in B-ALL dataset. The mechanistic

network of the inferred upstream regulator TGFB1. Genes presented in red are related to

genes that up-regulated in B-ALL dataset. Genes presented in green are related to genes

that down-regulated in B-ALL. The intensity of the colors is related to fold change

estimates. Arrows presented in orange, gray and yellow indicate activation, effect not

predicted and inconsistency, respectively.

51

Figure 6. The differentially expressed gene network with function in cell transformation.

Genes represented in red are upregulated in B-ALL group. The differentially expressed

gene network with function in cell transformation. Genes represented in red are

upregulated in B-ALL group. Genes presented in green are downregulated in B-ALL.

The intensity of the colors is related to fold change estimates. Arrows presented in

orange, gray and yellow indicate activation, effect not predicted and inconsistency,

respectively.

52

Figure 7. The differentially expressed gene network with function in proliferation of

cancer cells. The differentially expressed gene network with function in proliferation of

cancer cells. Genes represented in red are upregulated in B-ALL group. Genes presented

in green are downregulated in B-ALL. The intensity of the colors is related to fold change

estimates. Arrows presented in orange, gray and yellow indicate activation, effect not

predicted and inconsistency, respectively.

53

Table 1

Alternative splicing events in cancer Type of splicing

Gene Spliced isoform Type of cancer Citation

Cassette exons (skipping

one exon)

RON ΔRON (lacks exon 11) Breast and colon tumors Ghigna et al., 2005

Cassette exons (skipping on

multiple exons)

BRAF Skipping of exon 4-8 in

BRAFV600E

Melanoma Poulikakos et al., 2011

Cassette exons (exon

inclusion)

SYK SYK(L) includes exon 9 T-cell lymphomas, chronic

leukemias, head and neck carcinomas

Feldman et al. 2008;

Buchner et al., 2009;

Luangdilok et al., 2007

Alternative 5′ splice sites BCL2L1 BCL-XL Hepatocellular carcinoma, colorectal

cancer

Takehara et al., 2001;

Scherr et al., 2016

Alternative 3′ splice sites VEGF VEGFxxx Osteosarcoma Kaya et al., 2000

Intron retention HER2 Herstatin (results from intron 8

retention) and p100 (results from

intron 15 retention)

Breast cancer Jackson et al., 2013

Mutually exclusive exons ACTN1 Mutially exclusive exons - 19a and

19b

Colon cancer Gardina et al., 2006

54

Complex splicing patterns MDM2 more than 40 different splice

variants

Breast carcinoma, ovarian and

bladder cancers, glioblastoma

Bartel et al., 2002

55

Table 2

Patient characteristics

Patient ID

Blast rate (%) Age (month) WBC, 103/μl Sex Immunophenotype Cytogenetics

A18 97 17 4.3 F 19;10 46, XX-15der(1)

t(1;?),del(6)(q21),t mar

A19 88 36 3.7 M 19;10 hyperdiploidy

A20 92 120 3.6 M 19;10 46, XY

A23 96 180 2.3 M 19;10 46, XY del(6)(q21;q27)

A24 94 108 3.7 M 19;10 45, –7 –9 +der(9)

t(8;9)(q112;p11)

A26 91 48 4.3 M 19;10 47, XY

A30 94 24 3.7 F 19;10;20wk 46, XX

A36 91 72 2.7 F 19;10;20 46, XX

56

Table 3

Top twenty upregulated and down-regulated genes in B-ALL patients versus healthy donors

Upregulated genes Downregulated genes

Gene

Description

logFC FDR Gene Description logFC FDR

BIRC7 baculoviral IAP repeat

containing 7

12.58774792 1.79E-

22

CAMK2A calcium/calmodulin dependent

protein kinase II alpha

-11.33305197 1.35E-

31

FAM69C family with sequence

similarity 69 member C

12.57422628 7.50E-

32

CDH22 cadherin 22 -10.80719677 8.20E-

65

NOL4 nucleolar protein 4 12.42793145 3.68E-

17

ARSI arylsulfatase family member I -9.368798926 7.94E-

16

NRN1 neuritin 1 12.21760376 7.21E-

23

APLP1 amyloid beta precursor like protein 1 -8.874598807 1.50E-

38

NKAIN4 Sodium/potassium

transporting ATPase

interacting 4

12.07076779 7.33E-

20

ITLN1 intelectin 1 -8.322284896 1.56E-

54

PTGDR prostaglandin D2 receptor 11.78288729 4.21E-

51

WNT10A Wnt family member 10A -7.92759092 1.96E-

24

SDC2 syndecan 2 11.71967708 4.55E-

35

NPHS1 NPHS1, nephrin -7.712620956 1.06E-

36

BMP2 bone morphogenetic protein 2 11.63391213 3.27E-

26

CELA2A chymotrypsin like elastase family

member 2A

-7.115075267 2.05E-

34

CD2 CD2 molecule 11.60059176 2.56E-

21

CLLU1OS chronic lymphocytic leukemia up-

regulated 1 opposite strand

-7.106790442 1.13E-

07

57

RGMA repulsive guidance molecule

family member a

11.55426857 1.82E-

23

UPK2 uroplakin 2 -6.924019587 6.40E-

28

FCN1 ficolin 1 11.52363369 1.08E-

17

CD244 CD244 molecule -6.80438577 2.65E-

32

GIMAP6 GTPase, IMAP family

member 6

11.27208145 3.85E-

50

SEMA3F semaphorin 3F -6.785844561 7.67E-

38

CLIC5 chloride intracellular channel

5

11.26816477 5.10E-

21

NPPB natriuretic peptide B -6.77218967 3.03E-

30

MDGA2 MAM domain containing

glycosylphosphatidylinositol

anchor 2

11.22689521 2.85E-

20

ORM1 orosomucoid 1 -6.756404377 6.00E-

50

FAM19A5 family with sequence

similarity 19 member A5, C-

C motif chemokine like

11.22294095 8.46E-

21

CHADL chondroadherin like -6.740609261 2.00E-

45

FCGR3A Fc fragment of IgG receptor

IIIa

11.181703 1.07E-

17

TRPC5 transient receptor potential cation

channel subfamily C member 5

-6.732986733 8.19E-

36

LOXHD1 lipoxygenase homology

domains 1

11.11395696 4.14E-

18

LRRC18 leucine rich repeat containing 18 -6.641037404 3.59E-

27

KIF26A kinesin family member 26A 11.0719285 6.04E-

50

SLC36A3 solute carrier family 36 member 3 -6.519546438 1.76E-

28

GZMA granzyme A 10.91223894 2.40E-

18

ODF3L1 outer dense fiber of sperm tails 3 like

1

-6.379525998 4.72E-

28

TSHZ3 teashirt zinc finger homeobox

3

10.86466044 1.60E-

15

TNS4 tensin 4 -6.355462382 1.15E-

37

58

Table 4

Common transcripts that affected by DNA methylation Gene name logFC (untreated Nalm 6) logFC (treated Nalm 6) logFC (B-ALL patients)

CYTIP -11.86241096 -10.58620701 -3.320701712

TK1 -16.6756522 -15.03359745 -2.395212117

PLCG2 -16.38554599 -15.1004864 -2.32810002

SNN -10.95532228 -9.653917529 -2.110926604

PRDX5 -9.712780965 -8.174896096 2.038034986

COX8A -5.530015836 -4.317455614 2.063655717

UBQLN4 -10.21190755 -8.729855393 2.066030835

RNF181 -3.72386729 -2.09967035 2.115850312

TEX261 -9.351320334 -8.003411693 2.141855285

OSTC -11.92871196 -8.737987284 2.254406726

DAD1 -9.46691059 -5.870977984 2.261850469

PITPNC1 -5.764856109 -4.208504433 2.458969425

LDHA -13.23369912 -10.34328818 2.669660908

SDF2L1 2.551866594 3.711258169 2.671980184

IDH2 -6.207710831 -3.276596892 3.081108162

GAPDH -11.06919991 -9.238478066 3.301257076

59

S100A6 -8.971244012 -5.824001533 3.612659686

ISG15 -9.14616871 -4.3756427 3.698909038

PRDX1 -11.30244531 -7.869383782 4.400595351

60

Table 5

Gene ontology terms for common transcripts that affected by DNA methylation Gene name

GO term

CYTIP protein binding

TK1 thymidine kinase activity

PLCG2 signal transducer activity and phosphatidylinositol phospholipase C activity

SNN endosomal maturation

PRDX5 receptor binding and protein dimerization activity

COX8A cytochrome-c oxidase activity

UBQLN4 identical protein binding and damaged DNA binding

SUMO2 poly(A) RNA binding and SUMO transferase activity

RNF181 ligase activity and ubiquitin-protein transferase activity

TEX261 COPII adaptor activity

OSTC dolichyl-diphosphooligosaccharide-protein glycotransferase activity

DAD1 dolichyl-diphosphooligosaccharide-protein glycotransferase activity and oligosaccharyl transferase activity

PITPNC1 lipid binding and phosphatidylinositol transporter activity

LDHA oxidoreductase activity and L-lactate dehydrogenase activity

SDF2L1 chaperone binding and misfolded protein binding

IDH2 magnesium ion binding and oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor

GAPDH identical protein binding and NAD binding

S100A6 calcium ion binding and calcium-dependent protein binding

61

ISG15 protein tag

PRDX1 poly(A) RNA binding and identical protein binding

62

Table 6

Top canonical pathways identified by IPA Pathway

P-value Overlap

Protein kinase A signaling 1.55E-06 28.4 % (105/370)

Interferon signaling 3.26E-03 38.8 % (14/36)

Cyclins and cell cycle regulation 2.20E-03 32.5 % (25/77)

Phospholipase C signaling 1.56E-03 26.7 % (58/217)

Cell cycle control of chromosomal replication 4.39E-05 47.4 % (18/38)

63

GENERAL DISCUSSION

Alternative splicing plays a crucial role in numerous cellular and developmental

processes (Chen & Manley, 2009). In recent years, alternative splicing has been recognized as a

mechanism involved in many human disorders, including cancer (Singh & Cooper, 2012).

Changes in splicing patterns occur widely in cancer cells and has been shown to be associated

with resistance to therapeutic treatments (David & Manley, 2010).

Despite decades of leukemia research, there is still a need for reliable cancer biomarkers

for B-ALL diagnostics. The majority of pediatric B-ALL cases harbor gross numerical and

structural chromosomal alterations, but they do not explain all incidences of disease. Therefore

other molecular mechanisms likely contribute to B-ALL development, including alternative

splicing. RNA-seq analysis allows one to effectively and efficiently evaluate the entire

transcriptome by analyzing aberrant transcriptional patterns and splicing alterations that are

crucial for B-ALL pathogenesis. In combination with pathway analysis, alternatively spliced

transcripts may help better understand the molecular basis of post-transcriptional gene regulation

in the context of B-ALL.

Here, we employed a pathway-centered approach that allows one to characterize the

functional implications of differentially expressed and alternately spliced RNA transcripts in

pediatric B-ALL patients. A custom Perl script was designed to obtain a set of common gene

isoforms across individual B-ALL patients along with their corresponding transcript abundances.

The functional annotation and enrichment analyses in IPA identified aberrant activation of

cancer-related signaling pathways and transcriptional regulators associated with a B-ALL

malignant phenotype, such as ERBB2, TGFB1 and IL2. A distinctive feature of the common

64

gene isoforms which were identified, is their implication in oxidative phosphorylation and

mitochondrial dysfunction pathways. It has been shown, that mitochondrial damage modulates

alternative splicing in neuronal cells leading to changes in the abundance of certain isoforms

(Maracchioni et al., 2007). Therefore, mitochondrial dysfunction, a notable feature of cancer,

may also be the mechanism underlying the changes in alternative splicing patterns observed in

B-ALL patients.

Future directions for our research will integrate these findings with whole-genome DNA

methylation studies on B-ALL patients previously analyzed in our research group. Furthermore,

the leukemia-associated alternative splicing variants identified in this study may be utilized as

novel tools for the diagnosis and classification of leukemias and could also be the targets for

innovative therapeutical interventions based on highly selective splicing correction approaches.

65

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APPENDIX

Healthy donors

HCB11 HCB12 HCB13 HCB15 HCB16 HCB17 HCB18 HCB19

Raw reads 45370007 52383718 50788574 38083710 37026414 38475933 27610245 44975265

Total Reads aligned 40,061,716 44,788,079 43,627,385 32,333,070 33,434,852 35,013,099 25,263,374 42,006,898

Reads QC failed 0 0 0 0 0 0 0 0

Optical/PCR duplicate 0 0 0 0 0 0 0 0

Non Primary Hits 5,779,454 6,237,085 5,977,381 4,304,285 4,249,987 4,238,068 3,088,355 5,334,181

Unmapped reads 0 0 0 0 0 0 0 0

Multiple mapped reads 2,087,020 2,200,116 2,082,332 1,675,683 1,407,004 1,423,610 1,021,579 1,754,035

Uniquely mapped 37,974,696 42,587,963 41,545,054 30,657,387 32,027,848 33,589,490 24,241,795 40,252,862

% Uniquely mapped 83.7 81.3 81.8 80.5 86.5 87.3 87.8 89.5

Read-1 0 0 0 0 0 0 0 0

Read-2 0 0 0 0 0 0 0 0

Reads map to '+' 15,638,595 17,838,159 17,606,893 12,770,649 13,607,785 14,201,953 10,145,281 16,880,920

Reads map to '-' 18,966,339 21,061,997 20,303,875 15,277,217 15,688,415 16,501,251 11,904,178 19,862,999

Non-splice reads 31,228,571 34,479,087 33,782,113 24,502,591 26,414,387 27,618,743 19,577,648 32,658,679

Splice reads 3,376,363 4,421,069 4,128,655 3,545,275 2,881,813 3,084,461 2,471,811 4,085,240

Supplementary Table 1

88

B-ALL patients

B-ALL20 B-ALL23 B-ALL24 B-ALL26 B-ALL30 B-ALL37 B-ALL18 B-ALL19

Raw reads 50032958 54614253 56376631 42722037 52718310 63454534 42183675 53607546

Total Reads aligned 42,528,014 47,186,715 46,567,097 36,826,396 42,438,240 51,905,809 36,362,328 45,191,161

Reads QC failed 0 0 0 0 0 0 0 0

Optical/PCR duplicate 0 0 0 0 0 0 0 0

Non Primary Hits 5,019,186 6,256,098 5,684,306 5,044,960 5,000,916 6,601,771 4629827 5,119,497

Unmapped reads 0 0 0 0 0 0 0 0

Multiple mapped reads 2,301,516 2,785,327 2,480,572 2,007,936 2,372,324 3,045,818 1,856,082 2,358,732

Uniquely mapped 40,226,498 44,401,388 44,086,525 34,818,460 40,065,916 48,859,991 34,506,246 42,832,429

% Uniquely mapped 80.4 81.3 78.2 81.5 76 77 81.8 79.9

Read-1 0 0 0 0 0 0 0 0

Read-2 0 0 0 0 0 0 0 0

Reads map to '+' 18,450,843 20,359,828 20,270,776 15,929,453 18,472,492 22,521,427 15807483 19,730,537

Reads map to '-' 18,314,087 20,250,024 20,167,337 15,897,181 18,351,714 22,401,926 15770496 19,602,882

Non-splice reads 27,604,708 32,109,365 30,959,479 24,861,531 28,199,870 35,917,416 25273538 31,629,680

Splice reads 9,160,222 8,500,487 9,478,634 6,965,103 8,624,336 9,005,937 6304441 7,703,739


89

Healthy donors

HCB11 HCB12 HCB13 HCB15 HCB16 HCB17 HCB18 HCB19

Total splicing events 3589751 4699749 4389391 3772446 3064685 3279562 2626804 4351674

Known splicing events 3404878 4424894 4166434 3584675 2895880 3096573 2497134 4141947

% Known splicing events 94.849977 94.151709 94.920548 95.022566 94.49193 94.420322 95.06358 95.18054

Partial novel splicing events 130700 190516 155412 135738 121659 134038 96866 149544

% Partial novel splicing

events

3.6409210 4.0537484 3.5406278 3.598143 3.9697065 4.0870702 3.687599 3.436470

Novel splicing events 53286 83273 66588 51459 46477 48421 32362 59159

% Novel splicing events 1.4843926 1.7718605 1.5170213 1.3640752 1.5165343 1.4764472 1.231991 1.359453

Total splicing junctions 138558 153466 149824 134191 127733 133619 124285 138593

Known splicing junctions 109887 115208 115759 106629 101652 104470 101095 109554

%Known splicing junctions 0.27429429 0.2572291 0.2653356 0.3297831 0.3040301 0.298374 0.400164 0.2608

Partial novel splicing

junctions

21469 28066 25157 20874 19566 21695 17600 21281

Novel splicing junctions 7202 10192 8908 6688 6515 7454 5590 7758


90

B-ALL patients

B-ALL20 B-ALL23 B-ALL24 B-ALL26 B-ALL30 B-ALL37 B-ALL18 B-ALL19

Total splicing events 9851538 9144385 10181969 7488233 9277409 9675573 6784995 8263922

Known splicing events 9385328 8712925 9745204 7115059 8893958 9190657 6384977 7783632

% Known splicing events 95.267642 95.2816947 95.710407 95.016528 95.866831 94.9882451 94.104373 94.188110

Partial novel splicing events 289698 286133 298921 249899 264558 333204 263620 338216

% Partial novel splicing

events

2.9406372 3.12905679 2.9357878 3.3372225 2.8516367 3.44376504 3.8853381 4.0926814

Novel splicing events 175497 144090 136867 122425 117416 150800 135572 141449

% Novel splicing events 1.7814172 1.57572106 1.3442096 1.6348984 1.265612 1.55856403 1.9981149 1.7116449

Total splicing junctions 189195 193244 188890 190806 171837 202554 192485 201765

Known splicing junctions 143697 141811 140489 141994 132994 144514 137141 143879

%Known splicing junctions 0.3378878 0.30053162 0.30169155 0.38557669 0.31338246 0.27841585 0.3771513 0.3183786

Partial novel splicing

junctions

31236 35723 34680 35192 27770 42664 40063 44575

Novel splicing junctions 14262 15710 13721 13620 11073 15376 15281 13311


91

Number of reads

Group feature HCB11 HCB12 HCB13 HCB15 HCB16 HCB17 HCB18 HCB19

CDS_Exons 11530604 14581064 14007722 11639516 9755829 10127011 8275239 13354468

% CDS_Exons 28.44807942 31.6562045 31.3933102 34.599826 28.6016 28.278345 31.92736 30.84546

5'UTR_Exons 2767789 2630653 2525129 2392435 1571717 1909197 1540451 2311559

% 5'UTR_Exons 6.828634588 5.71127658 5.65917556 7.1117935 4.607873 5.3311813 5.9433369 5.3391196

3'UTR_Exons 3651463 4705182 4404389 3723887 3693864 4040499 2719995 5260576

% 3'UTR_Exons 9.008817703 10.2151807 9.87086623 11.069691 10.829466 11.282562 10.49423 12.150607

Introns 18400918 19451595 19768486 12532754 15610704 16223904 10792210 17367931

% Introns 45.39838301 42.230366 44.3040069 37.25508 45.766599 45.303116 41.638287 40.11555

% Intergenic 10.31608528 10.1869723 8.77264109 9.9636095 10.194462 9.8047956 9.9967869 11.549264

Total Tags 40532100 46060683 44620086 33640389 34109382 35811894 25918958 43294760


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Number of reads

Group feature B-ALL20 B-ALL23 B-ALL24 B-ALL26 B-ALL30 B-ALL37 B-ALL18 B-ALL19

CDS_Exons 27045723 26161597 28923849 21298072 25760423 28015015 19459838 23753628

% CDS_Exons 55.0504871 49.5986743 54.293404 51.290235 53.053297 48.4858147 48.130223 47.289245

5'UTR_Exons 1517741 1882774 2396034 1250153 2159858 2189243 1257368 1826434

% 5'UTR_Exons 3.08930108 3.56947225 4.4976325 3.0106312 4.4482029 3.7889407 3.1098615 3.6361049

3'UTR_Exons 9624804 8968305 9510804 6632308 8544238 9673994 6174012 7487293

% 3'UTR_Exons 19.5909035 17.0026333 17.852877 15.971992 17.596761 16.7428602 15.270249 14.905867

Introns 5610895 8816111 7334080 8702890 7075890 12207271 9557997 12372353

% Introns 11.4207523 16.714095 13.766915 20.958389 14.572715 21.1272234 23.639895 24.631152

% Intergenic 10.848556 13.1151251 9.5891711 8.7687534 10.329025 9.85516102 9.8497713 9.53763

Total Tags 49128944 52746565 53273228 41524614 48555744 57779817 40431639 50230508


93

VITA

Olha Kholod was born on May 19th, 1993 in Kyiv, Ukraine. Olha grew up with her

parents Volodymyr (Father) and Halyna (Mother) and her elder sister Mariia. Olha attended

Taras Shevchenko National University of Kyiv during 2010-2016. In June, 2014, Olha graduated

with a bachelor degree in Biology. In June, 2016, Olha graduated with master degree in Genetics

from the same institution. In May, 2015, Olha awarded Fulbright Graduate Scholarship. In

August 2015, Olha came to the University of Missouri, Columbia and joined Dr. Taylor’s

laboratory to pursue a Master of Science degree in Pathology. After she completes her M.S.

degree in May 2017, Olha will continue her post-academic training in Dr. Nathan Sheffield’s

laboratory at University of Virginia in Charlottesville.

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